This repository consists of:

• torchtext.data: Generic data loaders, abstractions, and iterators for text (including vocabulary and word vectors)
• torchtext.datasets: Pre-built loaders for common NLP datasets

Make sure you have Python 2.7 or 3.5+ and PyTorch 0.2.0 or newer. You can then install torchtext using pip:

pip install torchtext

If you want to use English tokenizer from SpaCy, you need to install SpaCy and download its English model:

pip install spacy
python -m spacy download en

Alternatively, you might want to use Moses tokenizer from NLTK. You have to install NLTK and download the data needed:

pip install nltk
python -m nltk.downloader perluniprops nonbreaking_prefixes

The data module provides the following:

• Ability to describe declaratively how to load a custom NLP dataset that's in a "normal" format:

  >>> pos = data.TabularDataset(
  ...    path='data/pos/pos_wsj_train.tsv', format='tsv',
  ...    fields=[('text', data.Field()),
  ...            ('labels', data.Field())])
  ...
  >>> sentiment = data.TabularDataset(
  ...    path='data/sentiment/train.json', format='json',
  ...    fields={'sentence_tokenized': ('text', data.Field(sequential=True)),
  ...            'sentiment_gold': ('labels', data.Field(sequential=False))})

• Ability to define a preprocessing pipeline:

  >>> src = data.Field(tokenize=my_custom_tokenizer)
  >>> trg = data.Field(tokenize=my_custom_tokenizer)
  >>> mt_train = datasets.TranslationDataset(
  ...     path='data/mt/wmt16-ende.train', exts=('.en', '.de'),
  ...     fields=(src, trg))

• Batching, padding, and numericalizing (including building a vocabulary object):

  >>> # continuing from above
  >>> mt_dev = data.TranslationDataset(
  ...     path='data/mt/newstest2014', exts=('.en', '.de'),
  ...     fields=(src, trg))
  >>> src.build_vocab(mt_train, max_size=80000)
  >>> trg.build_vocab(mt_train, max_size=40000)
  >>> # mt_dev shares the fields, so it shares their vocab objects
  >>>
  >>> train_iter = data.BucketIterator(
  ...     dataset=mt_train, batch_size=32,
  ...     sort_key=lambda x: data.interleave_keys(len(x.src), len(x.trg)))
  >>> # usage
  >>> next(iter(train_iter))
  <data.Batch(batch_size=32, src=[LongTensor (32, 25)], trg=[LongTensor (32, 28)])>

• Wrapper for dataset splits (train, validation, test):

  >>> TEXT = data.Field()
  >>> LABELS = data.Field()
  >>>
  >>> train, val, test = data.TabularDataset.splits(
  ...     path='/data/pos_wsj/pos_wsj', train='_train.tsv',
  ...     validation='_dev.tsv', test='_test.tsv', format='tsv',
  ...     fields=[('text', TEXT), ('labels', LABELS)])
  >>>
  >>> train_iter, val_iter, test_iter = data.BucketIterator.splits(
  ...     (train, val, test), batch_sizes=(16, 256, 256),
  >>>     sort_key=lambda x: len(x.text), device=0)
  >>>
  >>> TEXT.build_vocab(train)
  >>> LABELS.build_vocab(train)

The datasets module currently contains:

• Sentiment analysis: SST and IMDb
• Question classification: TREC
• Entailment: SNLI
• Language modeling: abstract class + WikiText-2
• Machine translation: abstract class + Multi30k, IWSLT, WMT14
• Sequence tagging (e.g. POS/NER): abstract class + UDPOS

Others are planned or a work in progress:

• Question answering: SQuAD

See the test directory for examples of dataset usage.